Predicting the von Neumann Entanglement Entropy using a Graph Neural Network

Anas Saleh

Calculating the von Neumann entanglement entropy from experimental data is challenging due to its dependence on the complete wavefunction, forcing reliance on approximations like classical mutual information (MI). We show that a neural network approach using a graph neural network is a suitable approach for predicting the von Neumann entropy from easily accessible experimental bitstrings.

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